基于模拟退火算法和改进灰狼优化器的异构无线传感器网络路由协议

doi:10.11959/j.issn.2096-3750.2021.00211

物联网学报 ›› 2021, Vol. 5 ›› Issue (2): 97-106.doi: 10.11959/j.issn.2096-3750.2021.00211

基于模拟退火算法和改进灰狼优化器的异构无线传感器网络路由协议

赵小强¹^,², 任少亚¹^,², 翟永智¹^,², 权恒¹^,², 杨婷¹^,²

¹ 西安邮电大学通信与信息工程学院，陕西西安 710121
² 陕西省信息通信网络及安全重点实验室，陕西西安 710121

修回日期:2020-10-15 出版日期:2021-06-30 发布日期:2021-06-01
作者简介:赵小强（1977- ），男，博士，西安邮电大学教授，主要研究方向为物联网技术及应用
任少亚（1994- ），女，西安邮电大学硕士生，主要研究方向为物联网技术及应用、路由协议
翟永智（1976- ），男，博士，西安邮电大学讲师，主要研究方向为物联网技术及应用
权恒（1994- ），男，西安邮电大学硕士生，主要研究方向为物联网技术及应用、智慧农业
杨婷（1998- ），女，西安邮电大学硕士生，主要研究方向为物联网技术及应用
基金资助:
国家自然科学基金资助项目(U1965102);陕西省创新人才推进计划—科技创新团队项目(2019TD-28);西安市科技计划项目(201806117YF05NC13-2);陕西省教育厅产业化培育项目(18JF029);陕西省国际合作计划项目(2018KW-025)

Heterogeneous wireless sensor network routing protocol based on simulated annealing algorithm and modified grey wolf optimizer

Xiaoqiang ZHAO¹^,², Shaoya REN¹^,², Yongzhi ZHAI¹^,², Heng QUAN¹^,², Ting YANG¹^,²

¹ School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
² Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an 710121, China

Revised:2020-10-15 Online:2021-06-30 Published:2021-06-01
Supported by:
The National Natural Science Foundation of China(U1965102);The Shaanxi Innovative Talent Promotion Plan-Science and Technology Innovation Team(2019TD-28);The Science and Technology Projects of Xi’an(201806117YF05NC13-2);The Industrialization Cultivation Project of Shaanxi Provincial Department of Education(18JF029);The Shaanxi International Cooperation Project(2018KW-025)

摘要/Abstract

摘要：

合理利用节点的能量异构特性延长网络生命周期是异构无线传感器网络（HWSN, heterogeneous wireless sensor network）的主要目标之一。因此，根据节点能量的异构性提出了一种基于模拟退火（SA, simulated annealing）算法和改进灰狼优化器（GWO, grey wolf optimizer）的HWSN路由协议SA-MGWO（SA-modified grey wolf optimizer）。首先，该协议通过为能量异构的节点定义不同的适应度函数进行初始簇的选取；然后计算节点的适应值，并将其视为灰狼优化器中的初始权重；同时，根据狼群与猎物的距离以及系数向量对权重进行动态更新，提高灰狼优化器的寻优能力；最后，利用模拟退火算法保证异构网络中最优簇集的选取。仿真结果表明，相比于SEP（stable election protocol）、分布式能量有效成簇（DEEC, distribute energy efficient clustering）、M-SEP及FIGWO（fitness value based improved grey wolf optimizer）协议，SA-MGWO协议的网络生命周期分别提高了53.1%、31.9%、46.5%和27.0%。

关键词: 异构无线传感器网络, 模拟退火算法, 灰狼优化器, 网络生命周期

Abstract:

It’s one of the main goals of the heterogeneous wireless sensor network (HWSN) to extend the network lifecycle by reasonably utilizing the heterogeneity of node energy.Therefore, according to the heterogeneity of node energy, a routing protocol (SA-MGWO) for HWSN based on simulated annealing (SA) algorithm and modified grey wolf optimizer (GWO) was proposed.Firstly, the appropriate initial clusters were selected by de?ning different ?tness functions for heterogeneous energy nodes.Secondly, The ?tness values of nodes were calculated and treated as initial weights in the GWO.At the same time, the weights were updated dynamically according to the distance between the wolves and their prey and coefficient vectors to improve the GWO’s optimization ability.Finally, simulated annealing algorithm was used to ensure the selection of optimal cluster set in heterogeneous networks.Compared with stable election protocol (SEP), distribute energy efficient clustering (DEEC), modified stable election protocol (M-SEP), and fitness value based improved grey wolf optimizer (FIGWO) protocols, the experimental results indicate that the network lifecycle of the SA-MGWO protocol improves by 53.1%, 31.9%, 46.5% and 27.0% respectively.

Key words: heterogeneous wireless sensor network, simulated annealing algorithm, grey wolf optimizer, network lifecycle

中图分类号:

赵小强, 任少亚, 翟永智, 权恒, 杨婷. 基于模拟退火算法和改进灰狼优化器的异构无线传感器网络路由协议[J]. 物联网学报, 2021, 5(2): 97-106.

Xiaoqiang ZHAO, Shaoya REN, Yongzhi ZHAI, Heng QUAN, Ting YANG. Heterogeneous wireless sensor network routing protocol based on simulated annealing algorithm and modified grey wolf optimizer[J]. Chinese Journal on Internet of Things, 2021, 5(2): 97-106.

图/表 9

图1

图2

图3

图4

表1

仿真参数"

实验参数	场景
网络区域	100 m ×100 m
网络中节点数目	100
基站位置	{50 m,50 m}
发送和接收数据报长度	4 000 bit
普通节点初始能量	0.5J
高级节点初始能量超过普通节点初始能量的倍数	1
高级节点占节点总数比例	1:10
最优簇首选取比例	1:10
单位比特发送能耗E_elec	50nJ bit
单位比特数据融合能耗E_DA	50nJ bit
自由空间模型下功率放大器损耗系数ε_fs	10 pJ/(bit?m ²)
多径衰减模型下功率放大器损耗系数ε_mp	0.0013 pJ/(bit?m ⁴)
适应度函数的权重	$a_{11} = \frac{1}{3}, a_{12} = \frac{1}{6}, a_{2} = \frac{1}{2}, a_{3} = 0.2$
目标优化函数的权重	$a_{41} = a_{42} = a_{43} = \frac{1}{3}$

表1

图5

图6

图7

图8

参考文献 15

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基于模拟退火算法和改进灰狼优化器的异构无线传感器网络路由协议

Heterogeneous wireless sensor network routing protocol based on simulated annealing algorithm and modified grey wolf optimizer

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